ricardokleinklein / abadia-gym

A simple stochastic OpenAI environment for training RL agents

This repository contains a PIP package which is an OpenAI environment for simulating an environment for the AbadIA (The abbey of crime game).

Also included some agents from random to more complex ones.

Pre-installation

We recommend use virtualenv in order to have a controlled (and tested/controlled) enviroment.

You can create this enviroment with:

virtualenv -p python3 python3

We're using python version 3 and the name of the directory with the enviroment will be python3. In this case it's created at the current directory.

The second step is invoke this enviroment with:

source ./python3/bin/activate

Note: that I created previuosly at the current directory this enviroment directory (python3) but you could create this wherever you wish.

Note2: you don't need to do that if you have python3 and you are not worried of messing with your current (and global) python enviroment.

Installation

For install AbadIA-gym just install all the dependencies from the requirements.txt file using pip3:

pip3 install -r requirements.txt 

Usage

import gym
import gym_abadia

env = gym.make('abadia-v0')

how to launch the game engine.

Clone the game engine project and do the usual things like read the README.md file and then launch the game engine:

./VigasocoSDL abadia -input:libVigasocoFakeInputPlugin.so,FakeInputPlugin  -audio:libVigasocoNULLAudioPlugin.so,NULLAudioPlugin

or use our not so cool bash shell loop:

./loopVigasocoSDL.sh

How to run the agent v4

run the version 4 agent QTables implementation and a few options to control the enviroment.

python3 agentv4_dqn.py --episodes=5 --steps=300 # --model=models/model_v1_lastest.model -c /tmp/check # -c partidas/20180602/abadia_checkpoint_180602_214255_980367_2_1_4_21_25_0.checkpoint

You can run it in a loop:

./loopagentv4.sh

Older versions of the agent

run the version 3 agent QTables implementation and a few options to control the enviroment.

python3 agentv3_qlearning.py --episodes=100 --steps=250 # -c partidas/20180429/abadia_checkpoint_18-04-29_19:53:02:726983_1_4_34_13_0.checkpoint

You can run it in a loop:

./loopagentv3.sh

** Note: You can expects lot of log info and some ascii art **

run the dumb agent:

(python3) ~/proyectos/abadia-gym (master ✘)✹✭ ᐅ python3 agentv1.py

run the version 2 agent a simple QTables implementation

(python3) ~/proyectos/abadia-gym (master ✘)✹✭ ᐅ python3 agentv2_qlearning.py